Five Most Popular Computer Science MOOCs Starting in Jan 2017

MOOCs provide an affordable and flexible way to learn new skills, pursue any interest through very scalable technology. Whether you’re interested in advancing your career, learning for yourself or leveraging online courses to educate your workforce, MOOCs can help. This post consist of top five popular computer science MOOCs in 2017.

If you are interested in developing your career in 2017 or if you want to start pursuing your interest in coding in 2017 then this list will be going to help you.

Here is the list of top 5 MOOCs starting in Jan – 2017 in Computer Science field. These courses vary from beginner’s level to advance level.

Top 5 MOOCs starting in Jan – 2017

Machine Learning:About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Functional Programming Principles in Scala: Functional programming is becoming increasingly widespread in industry. This trend is driven by the adoption of Scala as the main programming language for many applications. Scala fuses functional and object-oriented programming in a practical package. It interoperates seamlessly with both Java and Javascript. Scala is the implementation language of many important frameworks, including Apache Spark, Kafka, and Akka. It provides the core infrastructure for sites such as Twitter, Tumblr and also Coursera.In this course you will discover the elements of the functional programming style and learn how to apply them usefully in your daily programming tasks. You will also develop a solid foundation for reasoning about functional programs, by touching upon proofs of invariants and the tracing of execution symbolically.The course is hands on; most units introduce short programs that serve as illustrations of important concepts and invite you to play with them, modifying and improving them. The course is complemented by a series programming projects as homework assignments.Learning Outcomes. By the end of this course you will be able to:
– understand the principles of functional programming,
– write purely functional programs, using recursion, pattern matching, and higher-order functions,
– combine functional programming with objects and classes,
– design immutable data structures,
– reason about properties of functions,
– understand generic types for functional programs

Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line.

This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3.5. Even if you took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.

Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not “computation appreciation” courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.

Algorithms, Part I: This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

Introduction to Cyber Security:We shop online. We work online. We play online. We live online. As our lives increasingly depend on digital services, the need to protect our information from being maliciously disrupted or misused is really important.This free online course will help you to understand online security and start to protect your digital life, whether at home or work. You will learn how to recognise the threats that could harm you online and the steps you can take to reduce the chances that they will happen to you.With cyber security often in the news today, the course will also frame your online safety in the context of the wider world, introducing you to different types of malware, including viruses and trojans, as well as concepts such as network security, cryptography, identity theft and risk management.Your guide for the course is Cory Doctorow, a visiting professor at The Open University.

“If you want an introduction to the subject that provides an overview or to improve your own personal information security, it is an excellent course – enjoyable, informative, engaging and authoritative.”
– Tony Morbin, Editor-in-Chief, SC Magazine